Certain graphic manipulation applications are used to modify images depicting humans or other figures so that, for example, the features of a person's face are modified. For instance, a graphic manipulation application could modify one or more colors on a person's face, change the size of certain facial features, etc. But existing graphic manipulation applications involve limitations with respect to modifying hair features, such as facial hair or other image content depicting hair.
For instance, multiple images of a particular subject (i.e., a person with facial hair) could be used to generate a three-dimensional (“3D”) reconstruction of that subject's facial hair. The 3D reconstruction is then modified to change the appearance of individual hair strands. But accurately modeling hair through a 3D reconstruction of hair is difficult if a set of images (as opposed to a single image) is unavailable for the particular subject, and editing a 3D model generated from the 3D reconstruction is a time-consuming, laborious task. In another example, a deep neural network used by a graphic manipulation application is trained to learn how to encode different facial features (e.g., facial hair, age, expression etc.) into a feature space. The graphic manipulation application can be used to edit features within the feature space and thereby generate hair features for an image. But editing these vector-based features is not intuitive for an end user, and the resultant facial hair often lacks realism or other desirable details due to insufficient training data.
Therefore, existing solutions may involve disadvantages for reasons such as (but not limited to) those described above.